scholarly journals SPATIAL MOTION PATTERNS: ACTION MODELS FROM SEMI-DENSE TRAJECTORIES

2014 ◽  
Vol 28 (07) ◽  
pp. 1460011 ◽  
Author(s):  
THANH PHUONG NGUYEN ◽  
ANTOINE MANZANERA ◽  
MATTHIEU GARRIGUES ◽  
NGOC-SON VU
Keyword(s):  
Moving Objects ◽  
Computation Time ◽  
Local Binary Patterns ◽  
Point Particle ◽  
Spatial Motion ◽  
High Definition ◽  
Motion Patterns ◽  
Action Model ◽  
Global Representation ◽  
Action Models

A new action model is proposed, by revisiting local binary patterns (LBP) for dynamic texture models, applied on trajectory beams calculated on the video. The use of semi-dense trajectory field allows to dramatically reduce the computation support to essential motion information, while maintaining a large amount of data to ensure robustness of statistical bag of features action models. A new binary pattern, called Spatial Motion Pattern (SMP) is proposed, which captures self-similarity of velocity around each tracked point (particle), along its trajectory. This operator highlights the geometric shape of rigid parts of moving objects in a video sequence. SMPs are combined with basic velocity information to form the local action primitives. Then, a global representation of a space × time video block is provided by using hierarchical blockwise histograms, which allows to efficiently represent the action as a whole, while preserving a certain level of spatiotemporal relation between the action primitives. Inheriting from the efficiency and the invariance properties of both the semi-dense tracker Video extruder and the LBP-based representations, the method is designed for the fast computation of action descriptors in unconstrained videos. For improving both robustness and computation time in the case of high definition video, we also present an enhanced version of the semi-dense tracker based on the so-called super particles, which reduces the number of trajectories while improving their length, reliability and spatial distribution.

Determination of the Minimum Distance Between Moving Objects Including Velocity Information

1993 ◽  
Author(s):  
Meyer Nahon
Keyword(s):  
Minimum Distance ◽  
Moving Objects ◽  
Rapid Determination ◽  
Computation Time ◽  
Optimization Techniques ◽  
Real Time Control ◽  
Time Step ◽  
Time Control ◽  
Velocity Information

Abstract The rapid determination of the minimum distance between objects is of importance in collision avoidance for a robot maneuvering among obstacles. Currently, the fastest algorithms for the solution of this problem are based on the use of optimization techniques to minimize a distance function. Furthermore, to date this problem has been approached purely through the position kinematics of the two objects. However, although the minimum distance between two objects can be found quickly on state-of-the-art hardware, the modelling of realistic scenes entails the determination of the minimum distances between large numbers of pairs of objects, and the computation time to calculate the overall minimum distance between any two objects is significant, and introduces a delay which has serious repercussions on the real-time control of the robot. This paper presents a technique to modify the original optimization problem in order to include velocity information. In effect, the minimum distance calculation is performed at a future time step by projecting the effect of present velocity. This method has proven to give good results on a 6-dof robot maneuvering among obstacles, and has allowed a complete compensation of the lags incurred due to computational delays.

scholarly journals A Real Time Image Processing Bird Repellent System Using Raspberry Pi

2020 ◽  
Vol 5 (2) ◽  
Author(s):  
Oluwole Arowolo ◽  
Adefemi A Adekunle ◽  
Joshua A Ade-Omowaye
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Moving Objects ◽  
Detection System ◽  
Local Binary Patterns ◽  
Raspberry Pi ◽  
Feature Identification ◽  
Data Set ◽  
Haar Features ◽  
Haar Feature

Rice is one of the most consumed foods in Nigeria, therefore it’s production should be on the high as to meet the demand for it. Unfortunately, the quantity of rice produced is being affected by pests such as birds on fields and sometimes in storage. Due to the activities of birds, an effective repellent system is required on rice fields. The proposed effective repellent system is made up of hardware components which are the raspberry pi for image processing, the servo motors for rotation of camera for better field of view controlled by Arduino connected to the raspberry pi, a speaker for generating predator sounds to scare birds away and software component consisting of python and Open Cv library for bird feature identification. The model was trained separately using haar features, HOG (Histogram of Oriented Gradients) and LBP (Local Binary Patterns).Haar features resulted in the highest accuracy of 76% while HOG and LBP were, 27% and 72% respectively. Haar trained model was tested with two recorded real time videos with birds, the false positives were fairly low, about 41%. This haar feature trained model can distinguish between birds and other moving objects unlike a motion detection system which detects all moving objects. This proposed system can be improved to have a higher accuracy with a larger data set of positive and negative images. Keywords—Electronic pest repeller Haar cascade classifier, ultrasonic

scholarly journals Dynamic Autonomous Identification and Intelligent Lighting of Moving Objects with Discomfort Glare Limitation

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 7243
Author(s):  
Sebastian Słomiński ◽  
Magdalena Sobaszek
Keyword(s):  
Moving Objects ◽  
Tracking System ◽  
Electricity Consumption ◽  
Computation Time ◽  
Projection Image ◽  
Reduce Electricity Consumption ◽  
Smart Control ◽  
Discomfort Glare ◽  
Lighting Systems ◽  
Dynamic Lighting

The importance of reducing discomfort glare during the dynamic development of high luminance LEDs is growing fast. Smart control systems also offer great opportunities to reduce electricity consumption for lighting purposes. Currently, dynamic “intelligent” lighting systems are a rapidly developing field. These systems, consisting of cameras and lighting units, such as moving heads or multimedia projectors, are powerful tools that provide a lot of opportunities. The aim of this research is to demonstrate the possibilities of using the projection light in dynamic lighting systems that enable the reduction of discomfort glare and the light pollution phenomenon. The proposed system allows darkening or reducing the luminance of some sensitive zones, such as the eyes or the head, in real-time. This paper explores the development of the markerless object tracking system. The precise identification of the position and geometry of objects and the human figure is used for dynamic lighting and mapping with any graphic content. Time measurements for downloading the depth maps, as well as for identifying the human body’s position and pose, have been performed. The analyses of the image transformation times have been carried out in relation to the resolution of the images displayed by the projector. The total computation time related to object detection and image display translates directly into the precision of fitting the projection image to a moving object and has been shown.

scholarly journals Fast Moving Objects Detection Using iLBP Background Model

2014 ◽  
Vol XL-3 ◽  
pp. 347-350 ◽  
Author(s):  
B. Vishnyakov ◽  
V. Gorbatsevich ◽  
S. Sidyakin ◽  
Y. Vizilter ◽  
I. Malin ◽  
...  
Keyword(s):  
Moving Objects ◽  
Computational Algorithm ◽  
Image Representation ◽  
Local Binary Patterns ◽  
Surveillance Systems ◽  
New Approach ◽  
Moving Objects Detection ◽  
Proposed Model ◽  
Objects Detection ◽  
Background Models

In this paper a new approach for moving objects detection in video surveillance systems is proposed. It is based on iLBP (intensity local binary patterns) descriptor that combines the classic LBP (local binary patterns) and the multiple regressive pseudospectra model. The iLBP descriptor itself is considered together with computational algorithm that is based on the sign image representation. We show that motion analysis methods based on iLBP allow uniformly detecting objects that move with different speed or even stop for a short while along with unattended objects. We also show that proposed model is comparable to the most popular modern background models, but is significantly faster.

The Application of the Moving Object Detection in HD Video Surveillance System

2013 ◽  
Vol 385-386 ◽  
pp. 1509-1512
Author(s):  
Lian Li ◽  
Yong Peng Liu
Keyword(s):  
Video Surveillance ◽  
Surveillance System ◽  
Moving Objects ◽  
Processing System ◽  
Moving Object ◽  
Video Surveillance System ◽  
High Definition ◽  
Motion Direction ◽  
Distribution Features ◽  
Sign Distribution

Today the existing image processing systems widely used standard definition resolution. Which is not enough distinct. High definition (HD) and intelligence gradually become the developing trend of the image acquisition and processing system. Motion detection plays an important role in video surveillance system. The sign distribution features will be covered up by the use of the absolute differential image. In this article, a method to determine the motion direction of moving objects by using the sign distribution features in the differential image of two consecutive frames is proposed. To extract the characteristics of the moving object regions,Other parts as the background image is still. The transmission should been stopped, if there is no moving object. These should save storage space and reduce the demand for network speed. Experimental results show that algorithm of the method is suitable for computer processing.

scholarly journals Products of Variables in Structural Equation Models: Multiplicative Reticular Action Models

2019 ◽  
Author(s):  
Steven M. Boker ◽  
Timo von Oertzen ◽  
Andreas Markus Brandmaier
Keyword(s):  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Equation Model ◽  
Random Coefficients ◽  
Action Model ◽  
Action Models ◽  
Special Case ◽  
General Method

A general method is introduced in which variables that are products of other variables in the context of a structural equation model (SEM) can be decomposed into the sources of variance due to the multiplicands. The result is a new category of SEM which we call a Multiplicative Reticular Action Model (XRAM). XRAM can include interactions between latent variables, multilevel random coefficients, latent variable moderators, and novel constructs such as factors of paths and twin genetic decomposition of multilevel random coefficients. The method relies on an assumption that all variance sources in a model can be decomposed into linear combinations of independent normal standardized variables. Although the distribution of a variable that is an outcome of multiplication between other variables is not normal, the assumption is that it can be decomposed into sources that are normal if one takes into account the non-normality induced by the multiplication. The method is applied to an example to show how in a special case it is equivalent to known unbiased and efficient estimators in the statistical literature. Two simulations are presented that demonstrate the precision of the approximation and implement the method to estimate parameters in a multilevel autoregressive framework.

Convergence, continuity, recurrence and Turing completeness in dynamic epistemic logic1

2020 ◽  
Vol 30 (6) ◽  
pp. 1213-1238
Author(s):  
Dominik Klein ◽  
Rasmus K Rendsvig
Keyword(s):  
Epistemic Logic ◽  
Dynamic Epistemic Logic ◽  
Model Transformations ◽  
Continuous Mappings ◽  
Turing Completeness ◽  
Action Model ◽  
Free Case ◽  
Finite Action ◽  
Action Models ◽  
Turing Complete

Abstract The paper analyses dynamic epistemic logic from a topological perspective. The main contribution consists of a framework in which dynamic epistemic logic satisfies the requirements for being a topological dynamical system thus interfacing discrete dynamic logics with continuous mappings of dynamical systems. The setting is based on a notion of logical convergence, demonstratively equivalent with convergence in Stone topology. Presented is a flexible, parametrized family of metrics inducing the Stone topology, used as an analytical aid. We show maps induced by action model transformations continuous with respect to the Stone topology and present results on the recurrent behaviour of said maps. Among the recurrence results, we show maps induced by finite action models may have uncountably many recurrent points, even when initiated on a finite input model. Several recurrence results draws on the class of action models being Turing complete, for which the paper provides proof in the postcondition-free case. As upper bounds, it is shown that either 1 atom, 3 agents and preconditions of modal depth 18 or 1 atom, 7 agents and preconditions of modal depth 3 suffice for Turing completeness.

scholarly journals Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots

2020 ◽  
Author(s):  
Tomasz Piotr Kucner ◽  
Achim J. Lilienthal ◽  
Martin Magnusson ◽  
Luigi Palmieri ◽  
Chittaranjan Srinivas Swaminathan
Keyword(s):  
Mobile Robots ◽  
Spatial Motion ◽  
Motion Patterns ◽  
Probabilistic Mapping

scholarly journals Real-Time Detection and Recognition of Multiple Moving Objects for Aerial Surveillance

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1373 ◽  
Author(s):  
Wahyu Rahmaniar ◽  
Wen-June Wang ◽  
Hsiang-Chieh Chen
Keyword(s):  
Real Time ◽  
High Frequency ◽  
Moving Objects ◽  
Region Of Interest ◽  
Computation Time ◽  
Important Application ◽  
Feature Points ◽  
Aerial Surveillance ◽  
Precision Rate ◽  
Detection And Recognition

Detection of moving objects by unmanned aerial vehicles (UAVs) is an important application in the aerial transportation system. However, there are many problems to be handled such as high-frequency jitter from UAVs, small size objects, low-quality images, computation time reduction, and detection correctness. This paper considers the problem of the detection and recognition of moving objects in a sequence of images captured from a UAV. A new and efficient technique is proposed to achieve the above objective in real time and in real environment. First, the feature points between two successive frames are found for estimating the camera movement to stabilize sequence of images. Then, region of interest (ROI) of the objects are detected as the moving object candidate (foreground). Furthermore, static and dynamic objects are classified based on the most motion vectors that occur in the foreground and background. Based on the experiment results, the proposed method achieves a precision rate of 94% and the computation time of 47.08 frames per second (fps). In comparison to other methods, the performance of the proposed method surpasses those of existing methods.

ARMS: an automatic knowledge engineering tool for learning action models for AI planning

2007 ◽  
Vol 22 (2) ◽  
pp. 135-152 ◽  
Author(s):  
KANGHENG WU ◽  
QIANG YANG ◽  
YUNFEI JIANG
Keyword(s):  
Knowledge Engineering ◽  
Learning System ◽  
Propositional Satisfiability ◽  
Ai Planning ◽  
Model Learning ◽  
Frequent Sets ◽  
Action Model ◽  
Action Models ◽  
Definition Of ◽  
Modelling System

AbstractWe present an action model learning system known as ARMS (Action-Relation Modelling System) for automatically discovering action models from a set of successfully observed plans. Current artificial intelligence (AI) planners show impressive performance in many real world and artificial domains, but they all require the definition of an action model. ARMS is aimed at automatically learning action models from observed example plans, where each example plan is a sequence of action traces. These action models can then be used by the human editors to refine. The expectation is that this system will lessen the burden of the human editors in designing action models from scratch. In this paper, we describe the ARMS in detail. To learn action models, ARMS gathers knowledge on the statistical distribution of frequent sets of actions in the example plans. It then builds a weighted propositional satisfiability (weighted SAT) problem and solves it using a weighted MAXSAT solver. Furthermore, we show empirical evidence that ARMS can indeed learn a good approximation of the finally action models effectively.

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